30000mm is about 82mm per day. That’s a lot! Its hard to believe this value. Precipitation patterns have been fairly constant at most stations over the years. There appears to be a big uptick in precipitation at a handful of stations in the last 20 years. This means that a linear model for these locations may not be ideal, because rate of change in precipitation is not constant.
Precipitation is right-skewed. It would make sense to transform it to a log scale for model fitting.
We would expect that stations away from the coast are more likely than others to have the accasional run of dry years. These stations with zero precip are spread all around the country. The stations with a large number of zeros, have big blocks of 0s, eg ASN00007119 has about 34 years in a row of no rain. That’s not feasible. The culprit line of code is
summarise(prcp = mean(prcp, na.rm=T)*365) %>%
If there is any day with 0 rainfall in the year, and many missing values, the average will be returned as zero.
Intercept and slope are almost perfectly related. There are both locations where its getting wetter and drier. The most extreme are wet spots.
The increases in precipitation are mostly due to unusual amounts of rain in recent years. The decreases in precipitation are gradual declines.
It pretty much matches what BOM reports. The north west of the country appears to be getting wetter over the period, and the coastlines are getting a little drier.
# A tibble: 68 x 2
station n
<chr> <int>
1 ASN00007119 35
2 ASN00003028 24
3 ASN00005001 23
4 ASN00040385 21
5 ASN00005029 18
6 ASN00004068 17
7 ASN00004015 16
8 ASN00004008 14
9 ASN00010163 14
10 ASN00004046 11
# ... with 58 more rows
# A tibble: 68 x 2
station n
<chr> <int>
1 ASN00007119 35
2 ASN00003028 24
3 ASN00005001 23
4 ASN00040385 21
5 ASN00005029 18
6 ASN00004068 17
7 ASN00004015 16
8 ASN00004008 14
9 ASN00010163 14
10 ASN00004046 11
# ... with 58 more rows